I started with a basic map of room IDs to booking lists and thought I was on solid ground.
Start by clarifying requirements and defining core entities (Hotel, Room, Booking, Customer) and their relationships. Then design data structures and algorithms for search, booking, cancellation, and modification, focusing on efficiency and correctness. Finally, discuss trade-offs, edge cases, and potential optimizations.
Pro tip: Emphasize concurrency control and idempotency for booking operations, as real-world systems must handle simultaneous requests without double-booking. Also, consider using interval trees or segment trees for efficient date-range searches.
Ask questions to understand expected scale, consistency needs, and specific features (e.g., overbooking policy, payment handling). Define functional and non-functional requirements.
Identify core entities (Hotel, Room, RoomType, Booking, Customer) and their attributes. Define relationships and choose appropriate data structures (e.g., maps, sets, trees) for in-memory storage.
Outline algorithms for search (filtering by date, hotel, type, price), booking (checking availability, creating booking), cancellation, modification, and listing customer bookings. Consider time/space complexity.
Discuss handling of overlapping bookings, date validation, concurrent access (locks, transactions), and failure scenarios. Ensure idempotency for booking and cancellation.
Compare alternative data structures (e.g., interval trees vs. sorted lists for availability), indexing strategies, and caching. Mention scalability limits of in-memory approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the constraints: is this a single server or distributed system? Then propose a solution using in-memory synchronization primitives (e.g., mutexes, atomic operations) or a centralized lock service like Redis. Discuss trade-offs around consistency, availability, and scalability, and mention how you would handle failures and race conditions.
Pro tip: Emphasize that without a database, you need an external source of truth for coordination; a common pitfall is assuming a single server, so explicitly address distributed scenarios and how you'd achieve consensus (e.g., using Redis, ZooKeeper, or a consensus algorithm).
Ask about the scale, whether it's a single server or distributed, and the consistency requirements (e.g., strong vs eventual). This sets the stage for choosing the right approach.
Propose using an in-memory lock (e.g., mutex) for single-server, or a distributed lock service (e.g., Redis, etcd) for multi-server. Explain how it ensures only one client can book the room at a time.
Describe how to make the check-and-book operation atomic, such as using compare-and-swap (CAS) or Lua scripts in Redis. Mention the need for timeouts and idempotency to handle retries.
Address what happens if the lock service fails, network partitions occur, or the lock expires. Compare trade-offs: strong consistency vs availability, latency, and complexity.
Recap the chosen solution, why it fits the constraints, and any assumptions made. Optionally mention how a database would simplify this, but focus on the no-database constraint.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge that persistence is out of scope, then outline a snapshot-and-restore mechanism that serializes the in-memory state to durable storage on shutdown and deserializes it on startup. Focus on trade-offs like snapshot frequency, consistency, and performance impact, and mention how you would handle partial failures or versioning.
Pro tip: Emphasize that you would design the snapshot format to be backward-compatible and include a version number, so future changes don't break restores. Also, mention that you'd consider incremental snapshots or write-ahead logs if full snapshots are too heavy, showing you think about scalability.
Confirm that persistence is out of scope but snapshot/restore is a hypothetical. Ask about expected state size, acceptable downtime, and consistency requirements.
Select a format like JSON, Protobuf, or a binary format based on performance, readability, and schema evolution needs. Consider compression if state is large.
Decide when to snapshot (e.g., on graceful shutdown, periodically) and where to store it (local disk, object storage). Ensure atomic writes to avoid corruption.
On startup, load the snapshot, validate its integrity and version, and handle errors gracefully (e.g., fall back to empty state or alert).
Compare full vs. incremental snapshots, synchronous vs. asynchronous, and mention alternatives like write-ahead logs or event sourcing if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.